In terms of cost and execution time, data-driven Virtual Flow Meters (VFM) are alternative solutions to traditional well testing (WT) and physical multiphase flow meters (MPFM) for production rate determination which is needed for critical decisions by operators but faced with the challenge of low accuracy due to the transient and dynamic state of multiphase flow systems. Recently, some progress has been recorded by training steady state feed-forward neural networks to learn to approximate production rate based on certain number of input features (e.g., choke opening, pressure and temperature etc.) without any recursive feedback connection between the network outputs and inputs. This disconnection has impacted their accuracy. Dynamic artificial neural network, for example, the recurrent neural networks (RNN), e.g., LSTM has shown good performance as its architecture allows for the usage of data from the past time step to predict the current time step. Forecast accuracy for RNN are limited to short period of time due to their inherent vanishing gradient issues. While majority of VFM application have been developed for oil and gas systems, little or non is applied to gas condensate system. In this project, a sequence-to-sequence deep composite LSTM-Autoencoders was explored and used to demonstrate the ability of leveraging on its architecture to accurately predict multiphase flow rate for some wells in a gas condensate reservoir with highly dynamic multiphase flow phenomenon. A more complicated flow system was developed using a 3D compositional simulator to simulate, as close as possible, a realistic case of compositional reservoir. A single well was used to train the model and a blind test was ran on two other wells in same reservoir whose data are not part of the training set in order to predict their flow rate with accuracy. Based on the actual vs predicted results demonstrated, especially the blind test case, the feature extraction and encoding process of the trained LSTM-autoencoder was actually learning the physics of fluid flow and accurately passing the encoded results to the two decoders with very good output (training and testing mean square error are 0.02 and 0.05 respectively). The ability to leverage on some advanced artificial intelligence framework such as a composite LSTM-autoencoder has proven that it is possible to achieve the desired accuracy needed in data driven VFM to meet the requirement of low cost, low execution time and high accuracy. This project has also demonstrated the ability of the data driven model to learn the complex dynamics within the temporal ordering of input sequences of production data, with an internal memory adapted to remember or use information across long input sequences, hence, yield longer and reliable forecast, unlike other networks.
{"title":"Advances in Virtual Flow Metering Using Deep Composite Lstm-Autoencoder Network for Gas-Condensate Wells","authors":"J. Omeke","doi":"10.2523/iptc-22524-ms","DOIUrl":"https://doi.org/10.2523/iptc-22524-ms","url":null,"abstract":"\u0000 In terms of cost and execution time, data-driven Virtual Flow Meters (VFM) are alternative solutions to traditional well testing (WT) and physical multiphase flow meters (MPFM) for production rate determination which is needed for critical decisions by operators but faced with the challenge of low accuracy due to the transient and dynamic state of multiphase flow systems. Recently, some progress has been recorded by training steady state feed-forward neural networks to learn to approximate production rate based on certain number of input features (e.g., choke opening, pressure and temperature etc.) without any recursive feedback connection between the network outputs and inputs. This disconnection has impacted their accuracy. Dynamic artificial neural network, for example, the recurrent neural networks (RNN), e.g., LSTM has shown good performance as its architecture allows for the usage of data from the past time step to predict the current time step. Forecast accuracy for RNN are limited to short period of time due to their inherent vanishing gradient issues. While majority of VFM application have been developed for oil and gas systems, little or non is applied to gas condensate system.\u0000 In this project, a sequence-to-sequence deep composite LSTM-Autoencoders was explored and used to demonstrate the ability of leveraging on its architecture to accurately predict multiphase flow rate for some wells in a gas condensate reservoir with highly dynamic multiphase flow phenomenon. A more complicated flow system was developed using a 3D compositional simulator to simulate, as close as possible, a realistic case of compositional reservoir. A single well was used to train the model and a blind test was ran on two other wells in same reservoir whose data are not part of the training set in order to predict their flow rate with accuracy.\u0000 Based on the actual vs predicted results demonstrated, especially the blind test case, the feature extraction and encoding process of the trained LSTM-autoencoder was actually learning the physics of fluid flow and accurately passing the encoded results to the two decoders with very good output (training and testing mean square error are 0.02 and 0.05 respectively).\u0000 The ability to leverage on some advanced artificial intelligence framework such as a composite LSTM-autoencoder has proven that it is possible to achieve the desired accuracy needed in data driven VFM to meet the requirement of low cost, low execution time and high accuracy.\u0000 This project has also demonstrated the ability of the data driven model to learn the complex dynamics within the temporal ordering of input sequences of production data, with an internal memory adapted to remember or use information across long input sequences, hence, yield longer and reliable forecast, unlike other networks.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80324486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each stage, many wells in shale reservoirs have the "shut-in" process, which providing many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling the deep learning (DL) approach and field practices, we established a surrogate model for non-uniform fractures at one stage based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation method are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improve computational efficiency. The results show that the model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.67%. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model, which helps to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs.
{"title":"Pressure Transient Analysis in Shale Wells with Heterogeneous Fractures by Using a Deep Learning Based Surrogate Model","authors":"Zhiming Chen, Peng Dong, Tianyi Wang, Mingjin Cai, Yong Tian, Jiali Zhang","doi":"10.2523/iptc-22333-ms","DOIUrl":"https://doi.org/10.2523/iptc-22333-ms","url":null,"abstract":"\u0000 With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each stage, many wells in shale reservoirs have the \"shut-in\" process, which providing many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation.\u0000 To improve this situation, coupling the deep learning (DL) approach and field practices, we established a surrogate model for non-uniform fractures at one stage based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation method are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improve computational efficiency. The results show that the model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.67%. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model, which helps to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75272609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurately monitoring saturation change mechanisms requires adequate surveillance methods and techniques. We present a methodology to evaluate three-phase saturation using an advanced pulsed neutron measurement. This is a complex reservoir monitoring situation, where gas saturation must be monitored in addition to oil saturation, in a variable water salinity environment. An advanced pulsed neutron logging tool provided robust thermal neutron measurement (hydrogen index) for gas quantification. Formation capture cross section (sigma) was not used for water saturation because of its sensitivity to water salinity, which changes vertically and laterally in the subject field. The apparent volume of oil from the tool's improved-precision carbon/oxygen (C/O) method provided a salinity-independent indicator of oil saturation. Since this C/O apparent oil volume combines the carbon contributions from oil and gas, elemental modeling provided the apparent oil volume response to gas. Lithology information and porosity from initial formation evaluation were also entered in a linear solver to resolve water, oil, and gas volumes. This methodology was applied in wells where all three fluid saturations (water, oil, and gas) were expected to change over time. Surveys were taken at regular intervals over a span of several years. With the improved precision of the advanced pulsed neutron measurement, it was possible to precisely map the saturation changes with time in the field and identify variations in the fluids’ volumes down to a few porosity units. This information was critical in understanding fluid movements inside the reservoir. This is the first implementation of this technique. The precision brought by the advanced pulsed neutron tool provides superior results for monitoring a complex fluid mixture.
{"title":"Three-Phase Saturation Evaluation Using Advanced Pulsed Neutron Measurement","authors":"Ilies Mostefai, Marie Van Steene, Ali Al-Mulla","doi":"10.2523/iptc-22487-ms","DOIUrl":"https://doi.org/10.2523/iptc-22487-ms","url":null,"abstract":"\u0000 Accurately monitoring saturation change mechanisms requires adequate surveillance methods and techniques. We present a methodology to evaluate three-phase saturation using an advanced pulsed neutron measurement. This is a complex reservoir monitoring situation, where gas saturation must be monitored in addition to oil saturation, in a variable water salinity environment.\u0000 An advanced pulsed neutron logging tool provided robust thermal neutron measurement (hydrogen index) for gas quantification. Formation capture cross section (sigma) was not used for water saturation because of its sensitivity to water salinity, which changes vertically and laterally in the subject field. The apparent volume of oil from the tool's improved-precision carbon/oxygen (C/O) method provided a salinity-independent indicator of oil saturation. Since this C/O apparent oil volume combines the carbon contributions from oil and gas, elemental modeling provided the apparent oil volume response to gas. Lithology information and porosity from initial formation evaluation were also entered in a linear solver to resolve water, oil, and gas volumes.\u0000 This methodology was applied in wells where all three fluid saturations (water, oil, and gas) were expected to change over time. Surveys were taken at regular intervals over a span of several years. With the improved precision of the advanced pulsed neutron measurement, it was possible to precisely map the saturation changes with time in the field and identify variations in the fluids’ volumes down to a few porosity units. This information was critical in understanding fluid movements inside the reservoir.\u0000 This is the first implementation of this technique. The precision brought by the advanced pulsed neutron tool provides superior results for monitoring a complex fluid mixture.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73109610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geomechanical applications including wellbore stability evaluation, sanding assessment, and hydraulic fracturing design require rock mechanical properties (e.g. Young's modulus) as inputs. Significant discrepancy exists for the same property measured with various techniques due to different loading frequency and deformation amplitude applied, potentially resulting in added uncertainties in the applications. This paper presents the development of a prediction model enabling to determine mechanical properties consistently at any applied frequency. To build the prediction model, we first conducted measurements of Young's modulus and Poisson's ratio on sandstone samples over a wide frequency range from laboratory standard triaxial tests (~10−5 Hz), downhole logging (~20 KHz), to laboratory ultrasonic measurement (~1 MHz). These data provide a better understanding of frequency-dependent rock mechanical properties. Rock samples having different porosities and permeabilities are selected for investigating their effects on frequency-dependent acoustic wave velocities. Static measurements of Young's modulus and Poisson's ratio are also conducted to complete the measurements spectrum from static to dynamic frequencies. From the experimental data, the prediction model is developed to correlate rock elastic properties with measurement frequencies, which is further used to determine mechanical properties at any desired frequency for various geomechanically applications. As expected, the measured Young's modulus increases as the applied frequency increases, which is mainly due to the stiffening mechanism of the rock. The dispersion analysis of the results indicated a higher degree of stiffening for the higher porosity samples. The prediction model of Young's modulus vs the frequency was built and used to calculate the Young's modulus at the logging frequency from the available ultrasonic measurements. The predicted Young's modulus is compared well with the actual values obtained from acoustic logging data. On the opposite, Young's modulus at the ultrasonic frequency was calculated from the logging data using the prediction model and compared well with the measured Young's modulus at the ultrasonic frequency. Good agreement between the predicted and measured Young's moduli demonstrates the effectiveness of the prediction model, and its capability to derive the desired Young's modulus, such as the static, from the dynamic values measured from downhole logging data. The prediction model was developed from a physics based approach to derive the desired rock mechanical properties from their dynamic values measured at the logging or any other frequency, which potentially makes it unnecessary to develop traditional static vs dynamic correlations for various geomechanically applications.
{"title":"Frequency Dependent Rock Mechanical Properties for Geomechanical Applications","authors":"Shujath Ali Syed, G. Jin, Shouxiang Mark Ma","doi":"10.2523/iptc-22309-ms","DOIUrl":"https://doi.org/10.2523/iptc-22309-ms","url":null,"abstract":"\u0000 Geomechanical applications including wellbore stability evaluation, sanding assessment, and hydraulic fracturing design require rock mechanical properties (e.g. Young's modulus) as inputs. Significant discrepancy exists for the same property measured with various techniques due to different loading frequency and deformation amplitude applied, potentially resulting in added uncertainties in the applications. This paper presents the development of a prediction model enabling to determine mechanical properties consistently at any applied frequency. To build the prediction model, we first conducted measurements of Young's modulus and Poisson's ratio on sandstone samples over a wide frequency range from laboratory standard triaxial tests (~10−5 Hz), downhole logging (~20 KHz), to laboratory ultrasonic measurement (~1 MHz). These data provide a better understanding of frequency-dependent rock mechanical properties. Rock samples having different porosities and permeabilities are selected for investigating their effects on frequency-dependent acoustic wave velocities. Static measurements of Young's modulus and Poisson's ratio are also conducted to complete the measurements spectrum from static to dynamic frequencies. From the experimental data, the prediction model is developed to correlate rock elastic properties with measurement frequencies, which is further used to determine mechanical properties at any desired frequency for various geomechanically applications.\u0000 As expected, the measured Young's modulus increases as the applied frequency increases, which is mainly due to the stiffening mechanism of the rock. The dispersion analysis of the results indicated a higher degree of stiffening for the higher porosity samples. The prediction model of Young's modulus vs the frequency was built and used to calculate the Young's modulus at the logging frequency from the available ultrasonic measurements. The predicted Young's modulus is compared well with the actual values obtained from acoustic logging data. On the opposite, Young's modulus at the ultrasonic frequency was calculated from the logging data using the prediction model and compared well with the measured Young's modulus at the ultrasonic frequency. Good agreement between the predicted and measured Young's moduli demonstrates the effectiveness of the prediction model, and its capability to derive the desired Young's modulus, such as the static, from the dynamic values measured from downhole logging data. The prediction model was developed from a physics based approach to derive the desired rock mechanical properties from their dynamic values measured at the logging or any other frequency, which potentially makes it unnecessary to develop traditional static vs dynamic correlations for various geomechanically applications.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75410036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spontaneous imbibition is one of the key production mechanisms in fractured oil reservoirs. It is also an important process in tight gas formations, which has signi- ficant effects on the gas production after hydraulic fracturing. The objective of this research is to investigate the effects of pore throat sizes and connectivity on spontaneous imbibition behavior in tight carbonate rocks. Many plug samples were selected from various wells in the Middle East. The samples were characterized using X-ray CT imaging, thin-section photomicrographs, Helium porosity and gas permeability. High pressure mercury injection experiments (MICP) were performed in the primary drainage mode to obtain the pore throat size distributions, followed by mercury withdrawal tests to investigate the spontaneous imbibition curve and fluid trapping. The degree of pore connectivity was studied in the samples from thin-section photomicrographs and from primary drainage capillary pressure curves and were found in good relation with the mercury withdrawal behavior and residual fluid saturations. Higher permeability samples were characterized by lower entry pressures that showed higher tendency towards lower fluid (mercury) trapping. These results show important link between the rock nature and spontaneous imbibition and fluid trapping that can be deduced from mercury withdraw testing. Accurate prediction of spontaneous imbibition is crucial in many hydrocarbon reservoirs and such analyses help understand production mechanisms in different carbonate rock types.
{"title":"Pore Geometry Effect on Si, Trapping and Sor in Tight Carbonate Reservoirs","authors":"A. Kayali, S. Koronfol, David Gnozalez","doi":"10.2523/iptc-22360-ms","DOIUrl":"https://doi.org/10.2523/iptc-22360-ms","url":null,"abstract":"\u0000 Spontaneous imbibition is one of the key production mechanisms in fractured oil reservoirs. It is also an important process in tight gas formations, which has signi- ficant effects on the gas production after hydraulic fracturing. The objective of this research is to investigate the effects of pore throat sizes and connectivity on spontaneous imbibition behavior in tight carbonate rocks. Many plug samples were selected from various wells in the Middle East. The samples were characterized using X-ray CT imaging, thin-section photomicrographs, Helium porosity and gas permeability. High pressure mercury injection experiments (MICP) were performed in the primary drainage mode to obtain the pore throat size distributions, followed by mercury withdrawal tests to investigate the spontaneous imbibition curve and fluid trapping. The degree of pore connectivity was studied in the samples from thin-section photomicrographs and from primary drainage capillary pressure curves and were found in good relation with the mercury withdrawal behavior and residual fluid saturations. Higher permeability samples were characterized by lower entry pressures that showed higher tendency towards lower fluid (mercury) trapping.\u0000 These results show important link between the rock nature and spontaneous imbibition and fluid trapping that can be deduced from mercury withdraw testing. Accurate prediction of spontaneous imbibition is crucial in many hydrocarbon reservoirs and such analyses help understand production mechanisms in different carbonate rock types.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72600738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenhan Yue, Xinghao Wang, Juan Chen, Jie Tang, Guangjie Zheng, Long He
A real-time downhole microseismic mapping technique was recently used in the southern Sichuan shale gas development. The case study presented illustrates this technique and analyzes the results, from the geological evaluation through the engineering solution, for a typical H24 pad fracturing.
{"title":"Lessons Learned from a Case Study of Downhole Microseismic Mapping in the Southern Sichuan Shale Gas Play","authors":"Wenhan Yue, Xinghao Wang, Juan Chen, Jie Tang, Guangjie Zheng, Long He","doi":"10.2523/iptc-22515-ea","DOIUrl":"https://doi.org/10.2523/iptc-22515-ea","url":null,"abstract":"\u0000 A real-time downhole microseismic mapping technique was recently used in the southern Sichuan shale gas development. The case study presented illustrates this technique and analyzes the results, from the geological evaluation through the engineering solution, for a typical H24 pad fracturing.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73952588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eleonora Pignotti, Salvatore Spagnolo, S. Pilone, Gianni Baldassarri, P. Cappuccio, Alberto Valente, P. Greco, Mariangela Gonzalez Zamora
The objective of this paper is to demonstrate how a physics-based data driven model and inversion procedures can transform traditional ESP well monitoring into an indispensable tool for predicting multiphase flow rates in ESP production wells. Model and prediction techniques are evaluated by comparison with real field data, measured both live and retroactively from different ESP producing wells located in the South Europe producing field. Operational data commonly gathered by ESP gauge, such as Pressures data, Motor Current and Operative Frequency can be used to predict flow through ESP components, without need for rental of expensive Well Testing equipment. The exploitation of a similar advantage is made possible by the application of artificial intelligence algorithm joined with physics based modelling, taking in as input ESP dynamic data and giving as output a simulation–with acceptable accuracy- of the continuous downhole flow and reservoir properties, allowing the oil operator to obtain key information to optimize well production based on the calculation of ESP operational point. Such cost-effective metering technology is already suitable for online real-time systems implementation and has already been put in place in South Europe field, where it gives reliable results that will yield ongoing ESP run life improvement through its constant application. The improvement of several ESP KPIs, such as MTBF and MTTF, is strictly related to a more accurate follow up of the ESP operative point, hence of the ESP production. Higher ESP MTBF/MTTF might lead to a reduction of the number of necessary ESP replacement workover for year, thus causing the enhancement of hydrocarbon recovery and a reduction of the differed production.In addition to all of this, the possibility of virtual metering well production performance by means of a virtual model might provide a sensible reduction of the number of replacement systems provided from Service Companies, hence the overall optimization of production operation costs. The increasing need for operational efficiency, cost reduction and improved equipment means that service life has driven the recent technological developments related to electrical submersible pump (ESP) well operation management. This paper well described the application and the benefits of such technology to be used as reference successful case by other key players in the O&G market.
{"title":"Interpreting Downhole Esp Data for Predicting Production Performance by Use of Inversion-Based Methods in South Europe Field","authors":"Eleonora Pignotti, Salvatore Spagnolo, S. Pilone, Gianni Baldassarri, P. Cappuccio, Alberto Valente, P. Greco, Mariangela Gonzalez Zamora","doi":"10.2523/iptc-22241-ms","DOIUrl":"https://doi.org/10.2523/iptc-22241-ms","url":null,"abstract":"\u0000 The objective of this paper is to demonstrate how a physics-based data driven model and inversion procedures can transform traditional ESP well monitoring into an indispensable tool for predicting multiphase flow rates in ESP production wells. Model and prediction techniques are evaluated by comparison with real field data, measured both live and retroactively from different ESP producing wells located in the South Europe producing field.\u0000 Operational data commonly gathered by ESP gauge, such as Pressures data, Motor Current and Operative Frequency can be used to predict flow through ESP components, without need for rental of expensive Well Testing equipment. The exploitation of a similar advantage is made possible by the application of artificial intelligence algorithm joined with physics based modelling, taking in as input ESP dynamic data and giving as output a simulation–with acceptable accuracy- of the continuous downhole flow and reservoir properties, allowing the oil operator to obtain key information to optimize well production based on the calculation of ESP operational point.\u0000 Such cost-effective metering technology is already suitable for online real-time systems implementation and has already been put in place in South Europe field, where it gives reliable results that will yield ongoing ESP run life improvement through its constant application. The improvement of several ESP KPIs, such as MTBF and MTTF, is strictly related to a more accurate follow up of the ESP operative point, hence of the ESP production. Higher ESP MTBF/MTTF might lead to a reduction of the number of necessary ESP replacement workover for year, thus causing the enhancement of hydrocarbon recovery and a reduction of the differed production.In addition to all of this, the possibility of virtual metering well production performance by means of a virtual model might provide a sensible reduction of the number of replacement systems provided from Service Companies, hence the overall optimization of production operation costs.\u0000 The increasing need for operational efficiency, cost reduction and improved equipment means that service life has driven the recent technological developments related to electrical submersible pump (ESP) well operation management. This paper well described the application and the benefits of such technology to be used as reference successful case by other key players in the O&G market.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80851104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advent of global climate change, it has become incumbent on governments and industries to monitor and limit greenhouse gas emissions to prevent a catastrophic rise in the average global temperature. The Paris agreement [Paris 2015] aims to lower global greenhouse gas emissions by 40% (in comparison to greenhouse gas levels observed in 1990) by 2030. Methane is a greenhouse gas whose 100- year global warming potential is 25 times that of carbon dioxide [GWP] and whose atmospheric concentration has been increasing since 2007 [Nisbet 2016, Theo Stein, et al. 2021]. Thus, there is an increased requirement on industries from government regulators to detect, localize, quantify and mitigate both fugitive and vented emissions of methane. There are several different technologies that are available for automated methane emissions management. These include arial and ground-based mobile sensing units that are based on optical-gas imaging, satellite-based imagery [Jacob et al. 2016] and stationary metal-oxide based sensors. A key criterion that often needs to be satisfied is continuous monitoring for early detection and mitigation of fugitive leaks. Fixed metal-oxide based sensors [Yuliarto et al. (2015), Zeng et al. (2019), Yunusa et al. (2014), Potyrailo et al. (2020), Wang et al. (2010) and Feng et al. (2019)] are low-cost sensors that can be used for continuous monitoring of a site and are typically used for detection of leaks and alerting. The main challenge is to extend utility of these sensors to not only detect presence of fugitive and vented emissions, but also be able to estimate the number of leak sources and their probable locations and the total volume of hydrocarbon leaked over a period. This paper describes an approach used for detecting anomalies in emission data, identifying possible emission sources, and estimating emission leak rates using point measurements of concentration collected over a period along with measurements of wind speed and direction. This involves multiple analytics that combine concentration and wind-condition time-series data with physics models to predict the different outcomes.
随着全球气候变化的到来，监测和限制温室气体排放以防止全球平均气温灾难性上升已成为政府和行业义不容辞的责任。《巴黎协定》[2015年巴黎协定]旨在到2030年将全球温室气体排放量(与1990年观测到的温室气体水平相比)降低40%。甲烷是一种温室气体，其100年全球变暖潜势是二氧化碳[GWP]的25倍，其大气浓度自2007年以来一直在增加[Nisbet 2016, Theo Stein等，2021]。因此，政府监管机构对行业的要求越来越高，要求检测、定位、量化和减少甲烷的逃逸和排放。有几种不同的技术可用于自动化甲烷排放管理。其中包括基于光学气体成像的arial和地面移动传感单元，基于卫星的图像[Jacob etal . 2016]和固定式金属氧化物传感器。经常需要满足的一个关键标准是持续监测，以便及早发现和减轻泄漏。固定金属氧化物传感器[Yuliarto等人(2015)，Zeng等人(2019)，Yunusa等人(2014)，Potyrailo等人(2020)，Wang等人(2010)和Feng等人(2019)]是低成本传感器，可用于连续监测现场，通常用于检测泄漏和警报。主要的挑战是扩大这些传感器的应用范围，不仅要检测逸散物和排放物的存在，还要能够估计泄漏源的数量及其可能的位置，以及一段时间内泄漏的碳氢化合物总量。本文描述了一种用于检测排放数据异常，识别可能的排放源，并使用一段时间内收集的浓度点测量以及风速和风向测量来估计排放泄漏率的方法。这涉及多种分析，将浓度和风况时间序列数据与物理模型相结合，以预测不同的结果。
{"title":"Emission Source Detection and Leak Rate Estimation Using Point Measurements of Concentration","authors":"Arjun Roy, Sangeeta Nundy, Okja Kim, Godine Chan","doi":"10.2523/iptc-22377-ea","DOIUrl":"https://doi.org/10.2523/iptc-22377-ea","url":null,"abstract":"\u0000 \u0000 \u0000 With the advent of global climate change, it has become incumbent on governments and industries to monitor and limit greenhouse gas emissions to prevent a catastrophic rise in the average global temperature. The Paris agreement [Paris 2015] aims to lower global greenhouse gas emissions by 40% (in comparison to greenhouse gas levels observed in 1990) by 2030. Methane is a greenhouse gas whose 100- year global warming potential is 25 times that of carbon dioxide [GWP] and whose atmospheric concentration has been increasing since 2007 [Nisbet 2016, Theo Stein, et al. 2021]. Thus, there is an increased requirement on industries from government regulators to detect, localize, quantify and mitigate both fugitive and vented emissions of methane.\u0000 There are several different technologies that are available for automated methane emissions management. These include arial and ground-based mobile sensing units that are based on optical-gas imaging, satellite-based imagery [Jacob et al. 2016] and stationary metal-oxide based sensors. A key criterion that often needs to be satisfied is continuous monitoring for early detection and mitigation of fugitive leaks. Fixed metal-oxide based sensors [Yuliarto et al. (2015), Zeng et al. (2019), Yunusa et al. (2014), Potyrailo et al. (2020), Wang et al. (2010) and Feng et al. (2019)] are low-cost sensors that can be used for continuous monitoring of a site and are typically used for detection of leaks and alerting. The main challenge is to extend utility of these sensors to not only detect presence of fugitive and vented emissions, but also be able to estimate the number of leak sources and their probable locations and the total volume of hydrocarbon leaked over a period.\u0000 This paper describes an approach used for detecting anomalies in emission data, identifying possible emission sources, and estimating emission leak rates using point measurements of concentration collected over a period along with measurements of wind speed and direction. This involves multiple analytics that combine concentration and wind-condition time-series data with physics models to predict the different outcomes.\u0000","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90471095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanyan Chen, Yun Rui, Zheyuan Huang, Junjun Li, Yue Wang, Fei Liu, N. Bennett, Jing Mo
To understand formation structures extending away from the wellbore, azimuthal acoustic waveforms are acquired with longer recording length compared to conventional sonic logging. Advanced acoustic waveform processing algorithms such as 3D slowness time coherence (3D STC) and ray tracing applied to the reflection waveforms allow for quantitatively determining the true dip, azimuth, and position of the reflectors in 3D space, especially for far-field reflectors that can't be detected or located by conventional logging methods. In this paper we discuss two case studies of fracture evaluation. For the first one, experiences indicated that natural fractures bring operation risk for horizontal wells in shale gas play of Middle Yangtze Basin, such as casing deformation or screenout. Therefore, it was of great importance to evaluate natural fractures before completion and fracturing design. The borehole resistivity image log provided fracture assessment at the wellbore but cannot assess far-field fractures. The surface seismic ant track depicted fracture distribution on a large scale, yet with limited resolution. Azimuthal borehole acoustic reflection imaging filled the gap in between by identifying fractures as far as tens of meters from the wellbore. In the cased-hole horizontal well, the natural fracture results from azimuthal borehole acoustic reflection imaging confirmed the mud losses encountered while drilling. The operator used the results to optimize the completion design by placing perforation cluster about 15 m away from the natural fractures, and to change the fracturing design by adjusting slurry rate and fluid volume accordingly. For the second case, azimuthal borehole acoustic waveforms were acquired twice with the first run along an interval of Longmaxi shale gas in the vertical section of a 12.25-in. hole and the second run in a deviated section of an 8.5-in. hole. The result of the first run revealed a layer boundary between shale and carbonate. For the second run, high-dip-angle fractures in carbonate formations were identified with a maximum distance of 32 m from the wellbore. The dip and azimuth agreed with the few conductive fractures identified by the borehole resistivity image, yet the former identified more fractures than the latter. The two case studies clearly illustrate that azimuthal borehole acoustic imaging can quantitatively evaluate far-field fractures away from the wellbore, e.g., the true dip and azimuth, as well as position in 3D space. This helps not only provide a better reservoir characterization, but also allows optimization of the completion and fracturing design.
{"title":"Quantitatively Evaluating Far-Field Fractures by Analyzing Azimuthal Acoustic Waveforms: Case Studies in Vertical and Horizontal Wells","authors":"Yanyan Chen, Yun Rui, Zheyuan Huang, Junjun Li, Yue Wang, Fei Liu, N. Bennett, Jing Mo","doi":"10.2523/iptc-21910-ea","DOIUrl":"https://doi.org/10.2523/iptc-21910-ea","url":null,"abstract":"\u0000 To understand formation structures extending away from the wellbore, azimuthal acoustic waveforms are acquired with longer recording length compared to conventional sonic logging. Advanced acoustic waveform processing algorithms such as 3D slowness time coherence (3D STC) and ray tracing applied to the reflection waveforms allow for quantitatively determining the true dip, azimuth, and position of the reflectors in 3D space, especially for far-field reflectors that can't be detected or located by conventional logging methods.\u0000 In this paper we discuss two case studies of fracture evaluation. For the first one, experiences indicated that natural fractures bring operation risk for horizontal wells in shale gas play of Middle Yangtze Basin, such as casing deformation or screenout. Therefore, it was of great importance to evaluate natural fractures before completion and fracturing design. The borehole resistivity image log provided fracture assessment at the wellbore but cannot assess far-field fractures. The surface seismic ant track depicted fracture distribution on a large scale, yet with limited resolution. Azimuthal borehole acoustic reflection imaging filled the gap in between by identifying fractures as far as tens of meters from the wellbore. In the cased-hole horizontal well, the natural fracture results from azimuthal borehole acoustic reflection imaging confirmed the mud losses encountered while drilling. The operator used the results to optimize the completion design by placing perforation cluster about 15 m away from the natural fractures, and to change the fracturing design by adjusting slurry rate and fluid volume accordingly.\u0000 For the second case, azimuthal borehole acoustic waveforms were acquired twice with the first run along an interval of Longmaxi shale gas in the vertical section of a 12.25-in. hole and the second run in a deviated section of an 8.5-in. hole. The result of the first run revealed a layer boundary between shale and carbonate. For the second run, high-dip-angle fractures in carbonate formations were identified with a maximum distance of 32 m from the wellbore. The dip and azimuth agreed with the few conductive fractures identified by the borehole resistivity image, yet the former identified more fractures than the latter.\u0000 The two case studies clearly illustrate that azimuthal borehole acoustic imaging can quantitatively evaluate far-field fractures away from the wellbore, e.g., the true dip and azimuth, as well as position in 3D space. This helps not only provide a better reservoir characterization, but also allows optimization of the completion and fracturing design.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90986657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Zhi Kueh, Kok Liang Tan, Daevin Dev, Mohana Ramanee Thamilarasu, Syafiqa Abd Wahab, L. Riyanto, S. Hashim, Anandhadhasan Balasandran, T. Kristanto, C. Ramirez, Yee Choy Chen
Field A is mature hydrocarbon producing field located in east Malaysia discovered in 1963. With multistacked reservoirs more than 7,000 ft high, the reservoirs are predominantly friable and unconsolidated, requiring sand exclusion from the beginning. Most of the wells were completed using internal gravel pack (IGP) methods in the main reservoir. Being an aging producing field, many of the main reservoirs have been depleted and watered out, making the wells inactive. There are, however, several shallower marginal reservoirs, which have been bypassed and undeveloped, known as behind casing opportunity (BCO) reservoirs. The challenge is accessibility to this sand prone reservoir, which might require substantial workover operations, and thus higher costs. Remedial options with proven screen completion can be costly and economically difficult to justify. Mid-2020 marks seven and a half years since the application of a single treatment of epoxy resin in an idle well located in Field A as a remedial approach for BCO. The treatment, proven economically attractive by yielding cost savings of USD 5 million compared to the workover option, further supported by rigorous production monitoring, is unequivocally valuable based on the duration of sustained sand-free production, once again providing reassurance in making this solution a reliable sand-control remedial method for marginal reservoirs. It is important to note that the solution considered a range of laboratory data associated with the chemicals that effectively addressed the requirement based on the characteristics typical of this formation. Well test data from 2013 to 2019 supported sand-free production. Despite experiencing an increment of water cut percentages up to 93.29%, the well is still performing at acceptable production rates. The groundwork processes of candidate identification to the execution of converting the well are described, emphasizing technology comparisons applied in terms of resin fluid system type, execution plan, lessons learned, and best practices developed for maximizing the life of a sand-free producer well.
{"title":"Sand Consolidation Treatment: Durability in an Alternative Primary Sand Control Method for a Marginal Reservoir","authors":"Jing Zhi Kueh, Kok Liang Tan, Daevin Dev, Mohana Ramanee Thamilarasu, Syafiqa Abd Wahab, L. Riyanto, S. Hashim, Anandhadhasan Balasandran, T. Kristanto, C. Ramirez, Yee Choy Chen","doi":"10.2523/iptc-22318-ms","DOIUrl":"https://doi.org/10.2523/iptc-22318-ms","url":null,"abstract":"\u0000 Field A is mature hydrocarbon producing field located in east Malaysia discovered in 1963. With multistacked reservoirs more than 7,000 ft high, the reservoirs are predominantly friable and unconsolidated, requiring sand exclusion from the beginning. Most of the wells were completed using internal gravel pack (IGP) methods in the main reservoir. Being an aging producing field, many of the main reservoirs have been depleted and watered out, making the wells inactive. There are, however, several shallower marginal reservoirs, which have been bypassed and undeveloped, known as behind casing opportunity (BCO) reservoirs. The challenge is accessibility to this sand prone reservoir, which might require substantial workover operations, and thus higher costs. Remedial options with proven screen completion can be costly and economically difficult to justify.\u0000 Mid-2020 marks seven and a half years since the application of a single treatment of epoxy resin in an idle well located in Field A as a remedial approach for BCO. The treatment, proven economically attractive by yielding cost savings of USD 5 million compared to the workover option, further supported by rigorous production monitoring, is unequivocally valuable based on the duration of sustained sand-free production, once again providing reassurance in making this solution a reliable sand-control remedial method for marginal reservoirs.\u0000 It is important to note that the solution considered a range of laboratory data associated with the chemicals that effectively addressed the requirement based on the characteristics typical of this formation. Well test data from 2013 to 2019 supported sand-free production. Despite experiencing an increment of water cut percentages up to 93.29%, the well is still performing at acceptable production rates.\u0000 The groundwork processes of candidate identification to the execution of converting the well are described, emphasizing technology comparisons applied in terms of resin fluid system type, execution plan, lessons learned, and best practices developed for maximizing the life of a sand-free producer well.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87733488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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